Fairness and privacy in machine learning

Objective

With the ubiquitous deployment of machine learning algorithms in nearly every area of our lives, the problem of unethical or discriminatory algorithm-based decisions becomes more and more prevalent. To partially address these concerns, a new sub-field of machine learning has emerged. The goal of the course is to introduce the audience to recent developments of fairness aware algorithms. The emphasise will be made on those methods which are supported by statistical guarantees and that can be implemented in practice. We will study classification and regression problems under the so called demographic parity constraint—a popular way to define fairness of an algorithm. Several research directions will be proposed through out the course.